AI-Powered Biosignal Analytics & Remote Patient Monitoring – Hands-on Bootcamp

About This Course
The AI-Powered Biosignal Analytics & Remote Patient Monitoring – Hands-on Bootcamp is a practical, application-focused course designed to bridge the gap between medical wearables, biosignal processing, and real-world digital health solutions. Participants will learn how to work with ECG, PPG, IMU, and SpO₂ data, clean and analyze biosignals, extract meaningful features, build basic machine learning models, and create functional Remote Patient Monitoring (RPM) dashboards.

Aim
To equip participants with practical skills to process biosignals from medical wearables, build AI-driven analytics pipelines, and develop prototype Remote Patient Monitoring (RPM) dashboards for real-time health insights and decision support.

Course Objectives

• Introduce participants to the fundamentals of medical wearables and biosignal acquisition

• Provide hands-on experience in cleaning, preprocessing, and analyzing ECG, PPG, IMU, and SpO₂ data

• Teach practical methods for feature extraction, including heart rate, HRV, and activity metrics

• Enable participants to build basic machine learning models for health signal classification and prediction

• Guide learners in developing functional RPM dashboards with real-time data streams

• Equip participants with skills to create end-to-end digital health workflows—from sensors to analytics to monitoring

• Foster understanding of ethics, data privacy, and responsible AI practices in healthcare applications

Course Structure

✅ Module 1 – Basics & Health Signal Lab (App – Part 1)

• Intro to medical wearables (ECG, PPG, IMU, SpO₂) & use-cases

• Set up Vibe Code IDE, clone template, run starter app

• Import & clean wearable datasets (file upload, timestamps, basic stats)

• Explore signals (time-series plots, signal explorer)

• Improve signal quality (basic filters, raw vs filtered views)
  👉 Outcome: Health Signal Lab v1 – upload, clean, visualize, and filter biosignals

✅ Module 2 – Features, ML & Health Signal Lab (App – Part 2)

• Heart rate & HRV (peak detection, HR/HRV metrics)

• IMU feature extraction & activity summaries

• Build a simple ML pipeline (features, labels, LogReg/RandomForest)

• Auto “Train Model” + accuracy & confusion matrix in-app

• Generate summary report & mini-project using full pipeline
  👉 Outcome: Health Signal Lab v2 – full analytics + basic ML + reporting

✅ Module 3 – Remote Monitoring & Mini RPM Dashboard

• Intro to Remote Patient Monitoring (RPM) architecture & examples

• Set up Mini RPM Dashboard template in Vibe Code IDE

• Simulate multi-patient HR/SpO₂ streams & data ingestion

• Build live clinician dashboard (patient cards, time-series, auto-refresh)

• Add simple alert rules + discuss ethics, privacy & consent

• Participant demos of both apps
  👉 Outcome: Working Mini RPM Dashboard + end-to-end workflow from wearable data → analytics → remote monitoring

Who Should Enrol?

• Healthcare professionals & clinicians interested in medical wearables, digital health, and RPM

• Biomedical/biotechnology/healthcare researchers working with physiological signals or digital health data

• Data scientists, AI/ML engineers, and analysts applying ML to ECG, PPG, IMU, SpO₂ and biosignals

• Developers/engineers building health analytics tools, dashboards, and RPM applications

• Students (UG/PG/PhD) in engineering, life sciences, medicine, or computer science entering health-tech

Prerequisites (recommended, not mandatory):

• Basic familiarity with programming concepts (Python preferred)

• Interest in healthcare, biosignals, or AI/ML applications in medicine